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Neuroscience Brown Bag Series

Thursday, April 19, 2018,
  • Location: Wilson Hall • 111 21St Ave S • Nashville, TN 37240
  • Room: 316

Kianoush Banaie Boroujeni

Department of Psychology

Vanderbilt University

“Distinguishing Putative Interneuron Classes in Macaque Fronto-Striatal Circuit During Goal-Directed Behavior”

Neuronal interactions across fronto-striatal networks are essential to realize adaptive goal directed behavior. These neuronal interactions are based on circuits composed of multiple different inhibitory and excitatory neuron classes in the cortex, and on a heterogeneous set of inhibitory neurons in the purely inhibitory striatum. But how different cell classes contribute to front-striatal interactions is largely unknown. We therefore set out to address this question by measuring single cell activity in frontal and striatal areas of the nonhuman primate in vivo and to identify how cells differ in those measures that are directly observable from extracellular electrode recordings.

Directly observable are the cells’ action potential shape, their firing rate, and the temporal patterning of firing. Using these variables in cluster analysis, we distinguish seven different cell classes in the striatum and eight distinct cell classes in the frontal cortex. In my talk I will characterize how these cell classes differ and what their potential functional roles are in circuit operations. In particular, we show that we can reliably distinguish different putative prefrontal interneurons based on how regular, irregular or bursty they fire while the animals perform a attention demanding learning task. Intriguingly, our analysis also allows to distinguish different broad spiking neuron classes in the striatum that are known to be GABAergic subclasses important for action selection and reward dependent learning.

In summary, my talk outlines how extracellular recordings can be used to separate distinct classes of cells based on those firing patterns and action potential dynamics that constitute cell intrinsic properties. We believe that our classification needs to be considered in functional studies that aim to understand learning and information routing in fronto-striatal networks.